ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods

2024/09/25 by MaryBeth Defrance, Maarten Buyl, Defrance, MaryBeth +3 · 4 citations
Business, Management and Accounting · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Supply Chain Resilience and Risk Management

paper · pdf · doi:10.48550/arxiv.2409.16965

openalex publication_date 2024/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method tackles vary significantly, including the stage of intervention, the composition of sensitive features, the fairness notion, and the distribution of the output. Even in binary classification, these subtle differences make it highly complicated to benchmark fairness methods, as their performance can strongly depend on exactly how the bias mitigation problem was originally framed. Hence, we introduce ABCFair, a benchmark approach which allows adapting to the desiderata of the real-world problem setting, enabling proper comparability between methods for any use case. We apply ABCFair to a range of pre-, in-, and postprocessing methods on both large-scale, traditional datasets and on a dual label (biased and unbiased) dataset to sidestep the fairness-accuracy trade-off.

Cited by

• Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness
• Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs
• Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
• Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks

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